KOPF HALS – Automatisierte ASPECTS-Berechnung mittels KI – klinischer Nutzen trotz sinkender Relevanz zur Therapieentscheidung?
Bibliographic record
Abstract
Der Alberta Stroke Program Early CT Score (ASPECTS) ist die meistgenutzte Methode zur systematischen, quantitativen Auswertung nativer Schädel-CTs (NCCT) bei Verdacht auf akuten Schlaganfall. Die Genauigkeit und Konsistenz der Auswertung hängen dabei stark von der Expertise der Befunder*innen ab. Die Autor*innen der Studie entwickelten deshalb eine deep-learning (DL)-basierte KI und analysierten anschließend deren Einsatz im klinischen Alltag. Publication History Article published online: 19 August 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany Comment to this article: Kommentar zu „KOPF HALS – Automatisierte ASPECTS-Berechnung mittels KI –klinischer Nutzen trotz sinkender Relevanz zur Therapieentscheidung?“ Rofo 2025; 197(09): 1003-1004 DOI: 10.1055/a-2581-0126
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".